
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
import seaborn as sns


# Data source: https://www.kaggle.com/ngokulakannan/zomato-india-restaurants2-lakh-restaurants-data
df = pd.read_csv('indian_restaurants_details_cleaned_data.csv')
df.head(3)


# remove unused columns
df.drop(labels=['zomato_url', 'telephone', 'address', 'online_order',
                'table_reservation', 'delivery_only'], inplace=True, axis=1)
df.dropna(inplace=True)
df.head()


def filter_lat_long(df, sd=2):
  
  m_lo = df.longitude.mean()
  s_lo = df.longitude.std()
  m_la = df.latitude.mean()
  s_la = df.latitude.std()

  # keep only lat and long within 2 standard deviation (95%)
  df = df[(df.longitude > m_lo - sd * s_lo) & (df.longitude < m_lo + sd * s_lo)]
  df = df[(df.latitude > m_la - sd * s_la) & (df.latitude < m_la + sd * s_la)]

  return df


def get_city_restaurants(df, city='Delhi NCR', sd=2):
  city = df[df.city == city]
  city = filter_lat_long(city, sd)
  return city


df = filter_lat_long(df)
df.shape

(51091, 11)


df.city.unique()

array(['Mumbai', 'Delhi NCR', 'Kolkata', 'Bengaluru', 'Pune', 'Hyderabad',
       'Chennai', 'Lucknow', 'Ahmedabad', 'Jaipur', 'Chandigarh', 'Ooty',
       'Kochi', 'Indore', 'Shimla', 'Ludhiana', 'Gangtok', 'Kanpur',
       'Nashik', 'Visakhapatnam', 'Mangalore', 'Aurangabad', 'Coimbatore',
       'Dehradun', 'Amritsar', 'Nagpur', 'Rajkot', 'Surat', 'Vadodara',
       'Allahabad', 'Vijayawada', 'Jammu', 'Agra', 'Patna', 'Ranchi',
       'Ajmer', 'Neemrana', 'Cuttack', 'Gorakhpur', 'Madurai', 'Salem',
       'Bhopal', 'Manali', 'Mysore', 'Mussoorie', 'Trichy', 'Rishikesh',
       'Dharamshala', 'Jalandhar', 'Raipur', 'Udaipur', 'Patiala',
       'Haridwar', 'Puducherry', 'Varanasi', 'Pushkar', 'Goa',
       'Bhubaneswar', 'Kota', 'Siliguri', 'Meerut', 'Jabalpur', 'Manipal',
       'Gwalior', 'Vellore', 'Guntur', 'Jodhpur', 'Thrissur', 'Nainital',
       'Jamshedpur', 'Jhansi', 'Darjeeling'], dtype=object)


token = '### api-token ###'
px.set_mapbox_access_token(token)


delhi = df[df.city == 'Delhi NCR']


delhi = filter_lat_long(delhi)


delhi.describe()


def map_plot(df, title="Restaurant Locations"):
  fig = px.scatter_mapbox(df, lat='latitude', lon='longitude', hover_name='name', 
                        hover_data=['city', 'rating', 'cusine', 'cost_for_two'],
                        color_discrete_sequence=['blue'], zoom=10, opacity=0.5,
                         height=500, width=700, mapbox_style='outdoors', #mapbox_style='open-street-map',
                        title=title)

  fig.update_layout(margin={'r':0, 't':50, 'l':0, 'b':0})

  fig.show()


map_plot(delhi)


delhi_italian = delhi[delhi.cusine.str.contains('Italian')]
top_100_italian = delhi_italian.sort_values(by='rating', ascending=False)[:100]


fig = px.scatter_mapbox(top_100_italian, lat="latitude", lon="longitude", hover_name="name", 
                        hover_data=["rating", "cusine", "cost_for_two", "rating_count"],
                        color='rating', size='cost_for_two', zoom=10, opacity=0.5,
                        size_max=20,
                        color_continuous_scale=px.colors.sequential.Viridis,
                        mapbox_style='outdoors', height=500, width=700, 
                        title='Italian Restaurant Locations')

fig.update_layout(margin={"r":0, "t":50, "l":0, "b":0})
fig.show()


bangalore = get_city_restaurants(df, 'Bengaluru')


# merge different blocks of Koramangala into one
def change(x):
  if x.startswith('Koramangala'):
    return x.split(' ')[0]
  return x


bangalore.area = bangalore.area.map(change)


top_100_restaurants = bangalore.sort_values(by='rating', ascending=False)[:100]


bangalore_nc = bangalore.dropna(subset=['cusine'])
bangalore_italian = bangalore_nc[bangalore_nc.cusine.str.contains('Italian')]
top_100_italian = bangalore_italian.sort_values(by='rating', ascending=False)[:100]


from sklearn.cluster import KMeans


def get_clusters(df, n_clusters=12, top_n_clusters=5):
  x = df[['longitude', 'latitude']].values
  kmeans = KMeans(init="k-means++", n_clusters=n_clusters)
  kmeans.fit(x)

  hits = []
  for i, j in kmeans.cluster_centers_:
    k = (x[:, 0] > i - kmeans.inertia_) & (x[:, 0] < i + kmeans.inertia_) & (x[:, 1] > j - kmeans.inertia_) & (x[:, 1] < j + kmeans.inertia_)
    hits.append(sum(k))
  top_clusters = np.argsort(-np.array(hits))[:top_n_clusters]

  return top_clusters, kmeans


top_clusters, kmeans = get_clusters(top_100_restaurants, top_n_clusters=3)
r = kmeans.inertia_


fig = px.scatter_mapbox(top_100_restaurants, lat='latitude', lon='longitude', hover_name='name', 
                        hover_data=['city', 'rating', 'cusine', 'cost_for_two'],
                        color_discrete_sequence=['blue'], zoom=11, opacity=0.5,
                         height=500, width=700, mapbox_style='outdoors',
                        title='Top 100 restaurants in bangalore')

for i, j in kmeans.cluster_centers_[top_clusters]:
  dot = pd.DataFrame({'x': [i], 'y': [j], 'size': 1})
  marker = (px.scatter_mapbox(dot, lat='y', lon='x', size='size', size_max=int(r*3000),
                        color_discrete_sequence=['black'], zoom=11, opacity=0.2))
  fig.add_trace(marker.data[0])

fig.update_layout(margin={'r':0, 't':50, 'l':0, 'b':0})

fig.show()


top_clusters, kmeans = get_clusters(top_100_restaurants)

fig, ax = plt.subplots(figsize=(8, 8))
bangalore.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#fdcc8a', ax=ax)
top_100_restaurants.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#d7301f', ax=ax)
for i, j in kmeans.cluster_centers_[top_clusters]:
  circ = plt.Circle((i, j), kmeans.inertia_, edgecolor='#d7301f', fill=False)
  ax.add_patch(circ)
ax.set_aspect('equal', adjustable='datalim')
ax.set_title('Top 100 restaurants in bangalore');


top_clusters, kmeans = get_clusters(top_100_italian)

fig, ax = plt.subplots(figsize=(8, 8))
bangalore.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#fdcc8a', ax=ax, label='restaurants')
top_100_italian.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#d7301f', ax=ax, label='top restaurants')
for i, j in kmeans.cluster_centers_[top_clusters]:
  circ = plt.Circle((i, j), kmeans.inertia_, edgecolor='#d7301f', fill=False)
  ax.add_patch(circ)
ax.set_aspect('equal', adjustable='datalim')
ax.set_title('Top 100 italian restaurants in bangalore');


top_100_restaurants.area.value_counts()

Koramangala                      20
Indiranagar                      14
Whitefield                        9
Marathahalli                      8
HSR                               6
Malleshwaram                      5
Jayanagar                         4
Sarjapur Road                     4
BTM                               3
Brigade Road                      3
JP Nagar                          3
New BEL Road                      2
Kalyan Nagar                      2
Basavanagudi                      2
Rajajinagar                       2
Lavelle Road                      1
Varthur Main Road, Whitefield     1
Rajarajeshwari Nagar              1
MG Road                           1
Cunningham Road                   1
Church Street                     1
Vasanth Nagar                     1
Yeshwantpur                       1
Race Course Road                  1
Old Airport Road                  1
Banashankari                      1
Residency Road                    1
Richmond Road                     1
Name: area, dtype: int64


# split cuisine
df_list = list(np.array(top_100_restaurants))
df_1_list = []
for i in range(0,len(df_list)):
    string2 = df_list[i][5]
    try:
        string3 = string2.split(',')
    except:
        pass
    for j in range(len(string3)):
        weight = 1/len(string3) # since restaurant gets duplicated, give it less weight
        df_1_list.append(list(df_list[i])+[string3[j].strip()]+[weight])
    string3=[]


df_7 = pd.DataFrame(df_1_list, columns = ['name', 'city', 'area', 'rating', 'rating_count', 'cusine_old', 'cost_for_two',
       'time', 'popular food', 'longitude', 'latitude', 'cuisine', 'weight'])
df_7.drop(['cusine_old', 'time', 'popular food'], inplace=True, axis=1)


df_7.head(5)


df_7.weight.mean()

0.33112582781456956


df_7.weight = df_7.weight.map(lambda x: 0.3*2*x if x>=0.5 else x) # just a heuristic


df_7[(df_7.area == 'Koramangala') & (df_7.cuisine == 'Desserts')]


k = df_7.groupby(['area', 'cuisine']).size().reset_index(name='counts')
k['cost_for_two'] = df_7.groupby(['area', 'cuisine']).cost_for_two.mean().reset_index(name='mean')['mean']
k.head()


fig = px.treemap(k, path=[px.Constant('Bengalore'), 'area', 'cuisine'], values='counts', color_continuous_scale='RdBu', 
                 color='cost_for_two', title='Distribution of top rated restaurants')
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()


cost_less_than = 500
cheap_100_restaurants = bangalore[bangalore['cost_for_two'] < cost_less_than].sort_values(by=['rating'], ascending=False)[:100]


cheap_100_restaurants.head()


top_clusters, kmeans = get_clusters(cheap_100_restaurants, n_clusters=14, top_n_clusters=3)
r = kmeans.inertia_


fig = px.scatter_mapbox(cheap_100_restaurants, lat='latitude', lon='longitude', hover_name='name', 
                        hover_data=['city', 'rating', 'cusine', 'cost_for_two'],
                        color_discrete_sequence=['blue'], zoom=11, opacity=0.5,
                         height=500, width=700, mapbox_style='outdoors',
                        title='Top 100 restaurants with cost < $7 in bangalore')

for i, j in kmeans.cluster_centers_[top_clusters]:
  dot = pd.DataFrame({'x': [i], 'y': [j], 'size': 1})
  marker = (px.scatter_mapbox(dot, lat='y', lon='x', size='size', size_max=int(r*3000),
                        color_discrete_sequence=['black'], zoom=11, opacity=0.2))
  fig.add_trace(marker.data[0])

fig.update_layout(margin={'r':0, 't':50, 'l':0, 'b':0})

fig.show()


top_clusters, kmeans = get_clusters(cheap_100_restaurants, n_clusters=14)

fig, ax = plt.subplots(figsize=(8, 8))
bangalore.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#fdcc8a', ax=ax, label='restaurants')
cheap_100_restaurants.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#d7301f', ax=ax, label='top restaurants')
for i, j in kmeans.cluster_centers_[top_clusters]:
  circ = plt.Circle((i, j), kmeans.inertia_, edgecolor='#d7301f', fill=False)
  ax.add_patch(circ)
ax.set_aspect('equal', adjustable='datalim')
ax.set_title('Top 100 restaurants with cost < 500 in bangalore');


neighbourhood = (77.72, 12.98, 0.025)


my_nh_restaurants =  bangalore[(bangalore['longitude'] < neighbourhood[0]+neighbourhood[2]) & 
                               (bangalore['longitude'] > neighbourhood[0]-neighbourhood[2]) & 
                               (bangalore['latitude'] < neighbourhood[1]+neighbourhood[2]) & 
                               (bangalore['latitude'] > neighbourhood[1]-neighbourhood[2])]


my_nh_restaurants.shape

(456, 11)


top_my_nh_res = my_nh_restaurants.sort_values(by=['rating'], ascending=False)[:100]


top_my_nh_res.head()


popular_cuisine = {}
for cuisines in top_my_nh_res.cusine.unique():
  cuisines_list = cuisines.replace(' ', '').lower().split(',')
  for cuisine in cuisines_list:
    count = popular_cuisine.get(cuisine, 0)
    popular_cuisine[cuisine] = count + 1
print(popular_cuisine)

{'chinese': 18, 'asian': 12, 'sushi': 2, 'japanese': 2, 'thai': 3, 'continental': 17, 'european': 5, 'bbq': 6, 'american': 8, 'northindian': 28, 'salad': 7, 'mediterranean': 6, 'desserts': 14, 'icecream': 4, 'bakery': 6, 'fastfood': 13, 'southindian': 5, 'modernindian': 2, 'italian': 13, 'biryani': 5, 'beverages': 14, 'fingerfood': 7, 'momos': 3, 'cafe': 10, 'indonesian': 1, 'pizza': 6, 'african': 1, 'burger': 4, 'rolls': 1, 'healthyfood': 3, 'rajasthani': 1, 'vietnamese': 1, 'korean': 1, 'sandwich': 2, 'wraps': 1, 'lebanese': 2, 'paan': 1, 'kerala': 1, 'arabian': 2, 'mughlai': 3, 'seafood': 1, 'mithai': 1, 'streetfood': 4, 'mexican': 1, 'french': 1, 'tea': 2}


top_5_cuisine = [k for k, _ in sorted(popular_cuisine.items(), key=lambda item: -item[1])][:5]
top_5_cuisine

['northindian', 'chinese', 'continental', 'desserts', 'beverages']


fig, ax = plt.subplots(figsize=(8, 8))

bangalore.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#fdcc8a', ax=ax, label='restaurants')
top_my_nh_res.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, color='#d7301f', ax=ax, label='top restaurants')

ax.set_aspect('equal', adjustable='datalim')
ax.set_title('Restaurants in my neighbourhood');


plt.figure(figsize=(20, 10))
plt.barh(list(popular_cuisine.keys()), list(popular_cuisine.values()))
plt.xlabel('number of top restaurants')
plt.ylabel('cuisine')
plt.title('Popular cuisines in my neighbourhood');


df_nr = df.dropna(subset=['rating'])
most_expensive = df_nr.sort_values(by=['cost_for_two'], ascending=False)[:500]
least_expensive = df_nr.sort_values(by=['cost_for_two'], ascending=True)[:500]


plt.figure(figsize=(9, 6))
sns.histplot([most_expensive.rating.values, df_nr.rating.values], alpha=0.3,
             stat='density', kde=True, element='step', common_norm=False, binwidth=0.1)
# sns.histplot(, alpha=0.3, stat='density', kde=True)
plt.xlabel('rating', size=15)
plt.ylabel('restaurant density', size=15)
plt.legend(['usual', 'expensive'], loc='upper left')
plt.title('Is paying more for food worth?', size=15);


plt.figure(figsize=(9, 6))
plt.boxplot([most_expensive.rating.values, df_nr.rating.values]);
plt.ylabel('ratings', size=15)
plt.xticks([1, 2], ['expensive', 'usual'], size=15)
plt.title('Is paying more for food worth?', size=15);


import seaborn as sns


df['city'].unique()

array(['Mumbai', 'Delhi NCR', 'Kolkata', 'Bengaluru', 'Pune', 'Hyderabad',
       'Chennai', 'Lucknow', 'Ahmedabad', 'Jaipur', 'Chandigarh', 'Ooty',
       'Kochi', 'Indore', 'Shimla', 'Ludhiana', 'Gangtok', 'Kanpur',
       'Nashik', 'Visakhapatnam', 'Mangalore', 'Aurangabad', 'Coimbatore',
       'Dehradun', 'Amritsar', 'Nagpur', 'Rajkot', 'Surat', 'Vadodara',
       'Allahabad', 'Vijayawada', 'Jammu', 'Agra', 'Patna', 'Ranchi',
       'Ajmer', 'Neemrana', 'Cuttack', 'Gorakhpur', 'Madurai', 'Salem',
       'Bhopal', 'Manali', 'Mysore', 'Mussoorie', 'Trichy', 'Rishikesh',
       'Dharamshala', 'Jalandhar', 'Raipur', 'Udaipur', 'Patiala',
       'Haridwar', 'Puducherry', 'Varanasi', 'Pushkar', 'Goa',
       'Bhubaneswar', 'Kota', 'Siliguri', 'Meerut', 'Jabalpur', 'Manipal',
       'Gwalior', 'Vellore', 'Guntur', 'Jodhpur', 'Thrissur', 'Nainital',
       'Jamshedpur', 'Jhansi', 'Darjeeling'], dtype=object)


major_cities = df[(df.city=='Delhi NCR') |
                  (df.city=='Mumbai') |
                  (df.city=='Bengaluru') |
                  (df.city=='Kolkata')]
major_cities = major_cities[major_cities.cusine.str.contains('North Indian')].sample(1000)


major_cities.head()


plt.figure(figsize=(15, 8))
sns.violinplot(x="city", y="rating", data=major_cities, inner=None)
sns.swarmplot(x="city", y="rating", data=major_cities, color="white", edgecolor="gray", size=3)

<AxesSubplot:xlabel='city', ylabel='rating'>


def filter_cost(df):
  
  m = df.cost_for_two.mean()
  s = df.cost_for_two.std()

  # keep only within 2 standard deviation (95%)
  # df = df[(df.cost_for_two > m - 5 * s) & (df.cost_for_two < m + 5 * s)]
  df = df[df.cost_for_two <= 1500]

  return df


delhi = filter_cost(get_city_restaurants(df, 'Delhi NCR'))
bengaore = filter_cost(get_city_restaurants(df, 'Bengaluru'))
kolkata = filter_cost(get_city_restaurants(df, 'Kolkata'))
mumbai = filter_cost(get_city_restaurants(df, 'Mumbai'))


def get_bins(df):
  costs = df.cost_for_two.values
  num = np.histogram(costs, bins=np.arange(0, 1501, 100))[0] / len(costs)
  return np.insert(num, 0, 0)


delhi_bins = get_bins(delhi)
bengaore_bins = get_bins(bengaore)
kolkata_bins = get_bins(kolkata)
mumbai_bins = get_bins(mumbai)


fig, ax = plt.subplots(figsize=(10, 7))
x = np.arange(0, 1501, 100)
y = [delhi_bins, bengaore_bins, kolkata_bins, mumbai_bins]
ax.stackplot(x, y);


fig, ax = plt.subplots(figsize=(10, 7))
ax.stackplot(x, y, baseline="sym")
ax.axhline(0, color="black", ls="--");


from scipy import stats


def gaussian_smooth(x, y, grid, sd):
    weights = np.transpose([stats.norm.pdf(grid, m, sd) for m in x])
    weights = weights / weights.sum(0)
    return (weights * y).sum(1)


fig, ax = plt.subplots(figsize=(10, 7))
grid = np.linspace(0, 1501, num=500)
COLORS = ["#A6BDDB", "#74A9CF", "#2B8CBE", "#045A8D"]
y_smoothed = [gaussian_smooth(x, y_, grid, 60) for y_ in y]
ax.stackplot(grid, y_smoothed, colors=COLORS, baseline="sym")
plt.legend(['Delhi', 'Bengalore', 'Kolkata', 'Mumbai'])
plt.xlabel('cost', size=15)
plt.ylabel('restaurant density norm', size=15)
plt.title('Cost of food across cities', size=15);


import plotly.figure_factory as ff

hist_data = [delhi.cost_for_two.values, mumbai.cost_for_two.values, 
             bengaore.cost_for_two.values, kolkata.cost_for_two.values]

group_labels = ['Delhi', 'Mumbai', 'Bengalore', 'Kolkata']
# colors = ['#333F44', '#37AA9C', '#94F3E4']

# Create distplot with curve_type set to 'normal'
fig = ff.create_distplot(hist_data, group_labels, show_hist=False, bin_size=100, histnorm='probability')
# fig = ff.create_distplot(hist_data, group_labels, colors=COLORS, bin_size=100, show_curve=False)

# Add title
fig.update_layout(title_text='Cost distribution between cities',
                  height=700, width=1000, xaxis_title="Cost",
                  yaxis_title="Probability",)
fig.show()


popular_cuisine = {}
for cuisines in df.cusine.unique():
  cuisines_list = cuisines.split(', ')
  for cuisine in cuisines_list:
    count = popular_cuisine.get(cuisine, 0)
    popular_cuisine[cuisine] = count + 1
print(popular_cuisine)

{'Street Food': 740, 'Chinese': 4409, 'Fast Food': 2560, 'North Indian': 4996, 'Pizza': 931, 'Panini': 6, 'Sandwich': 642, 'South Indian': 1427, 'Italian': 2203, 'Continental': 2530, 'Asian': 841, 'Biryani': 1325, 'Mughlai': 1019, 'Kebab': 427, 'Bakery': 410, 'Desserts': 1280, 'Bengali': 166, 'American': 553, 'Cafe': 1177, 'Rolls': 549, 'Healthy Food': 468, 'Mithai': 281, 'Momos': 535, 'Ice Cream': 224, 'Mexican': 601, 'Beverages': 1730, 'Seafood': 955, 'Burger': 811, 'Rajasthani': 98, 'Kerala': 212, 'Andhra': 215, 'Finger Food': 350, 'Thai': 606, 'European': 428, 'Malwani': 128, 'Nepalese': 21, 'Bangladeshi': 4, 'Paan': 7, 'Juices': 262, 'Lebanese': 279, 'Japanese': 217, 'BBQ': 225, 'Salad': 602, 'Burmese': 50, 'Lucknowi': 66, 'Bihari': 23, 'Mediterranean': 272, 'Roast Chicken': 20, 'Wraps': 140, 'Modern Indian': 124, 'Ethiopian': 3, 'Hyderabadi': 142, 'Sushi': 103, 'Chettinad': 117, 'Kashmiri': 21, 'Middle Eastern': 52, 'Tibetan': 86, 'Mishti': 20, 'Korean': 52, 'Indian': 117, 'Parsi': 44, 'Maharashtrian': 168, 'Konkan': 58, 'Bar Food': 111, 'Pakistani': 2, 'Afghan': 66, 'Goan': 225, 'French': 59, 'Gujarati': 79, 'Raw Meats': 17, 'Portuguese': 14, 'Awadhi': 69, 'Belgian': 12, 'Indonesian': 41, 'Arabian': 279, 'Turkish': 34, 'Singaporean': 20, 'Mangalorean': 121, 'Iranian': 26, 'Coffee': 104, 'North Eastern': 19, 'Armenian': 1, 'Tea': 107, 'Assamese': 12, 'Vietnamese': 64, 'Naga': 16, 'Steak': 82, 'Spanish': 23, 'Brazilian': 5, 'Frozen Yogurt': 16, 'Tex-Mex': 31, 'Peruvian': 2, 'South American': 9, 'Bubble Tea': 21, 'Poké': 14, 'Sindhi': 12, 'Malaysian': 88, 'Cantonese': 16, 'British': 11, 'Oriental': 3, 'Tamil': 4, 'Odia': 24, 'Egyptian': 3, 'Moroccan': 7, 'Greek': 15, 'Hot dogs': 5, 'German': 13, 'Russian': 5, 'Bohri': 7, 'Charcoal Chicken': 16, 'Mongolian': 6, 'Sri Lankan': 13, 'Fried Chicken': 1, 'Cuisine Varies': 4, 'Vegetarian': 1, 'Coffee and Tea': 3, 'Fusion': 4, 'Cafe Food': 6, 'Soul Food': 1, 'Grill': 8, 'African': 5, 'Pan Asian': 2, 'Drinks Only': 2, 'Himachali': 1, 'Crepes': 1, 'Israeli': 3, 'International': 1, 'Irish': 1, 'Falafel': 1}


top_10_cuisine = [k for k, _ in sorted(popular_cuisine.items(), key=lambda item: -item[1])][:10]
top_10_cuisine

['North Indian',
 'Chinese',
 'Fast Food',
 'Continental',
 'Italian',
 'Beverages',
 'South Indian',
 'Biryani',
 'Desserts',
 'Cafe']


top_10_cities = list(df.city.value_counts().index[:10].values)
top_10_cities

['Delhi NCR',
 'Mumbai',
 'Bengaluru',
 'Pune',
 'Kolkata',
 'Hyderabad',
 'Chennai',
 'Ahmedabad',
 'Jaipur',
 'Chandigarh']


df_heatmap = pd.DataFrame(dict(city=top_10_cities))


for cuisine in top_10_cuisine:
  agg = []
  for city in top_10_cities:
    avg_cost = df[(df.city == city) & (df.cusine.str.contains(cuisine))].cost_for_two.mean()
    agg.append(avg_cost)
  df_heatmap[cuisine] = pd.Series(agg)


df_heatmap = df_heatmap.set_index('city')
df_heatmap


sns.clustermap(df_heatmap, standard_scale=1, cmap='RdBu');


bangalore.head()


top_restaurants = bangalore.sort_values(by='rating', ascending=False)[:500]


popular_cuisine = {}
for cuisines in top_restaurants.cusine.unique():
  cuisines_list = cuisines.split(', ')
  for cuisine in cuisines_list:
    count = popular_cuisine.get(cuisine, 0)
    popular_cuisine[cuisine] = count + 1
print(popular_cuisine)

{'European': 28, 'Mediterranean': 23, 'North Indian': 123, 'BBQ': 23, 'Continental': 107, 'Italian': 62, 'South Indian': 31, 'Finger Food': 26, 'Pizza': 31, 'Ice Cream': 14, 'Desserts': 48, 'Asian': 63, 'Chinese': 74, 'Thai': 21, 'Momos': 8, 'Sushi': 10, 'Japanese': 17, 'Healthy Food': 12, 'Salad': 21, 'Steak': 7, 'Bar Food': 4, 'Cafe': 45, 'Kebab': 3, 'Beverages': 44, 'Burmese': 4, 'American': 31, 'Tex-Mex': 3, 'Burger': 24, 'Mexican': 8, 'Tea': 6, 'Modern Indian': 16, 'Mughlai': 14, 'Bakery': 12, 'Seafood': 22, 'Biryani': 21, 'Goan': 2, 'Spanish': 2, 'Cantonese': 1, 'Lucknowi': 1, 'Korean': 7, 'Fast Food': 30, 'Street Food': 6, 'Vietnamese': 5, 'Andhra': 7, 'Mangalorean': 9, 'Konkan': 3, 'Kerala': 3, 'Sandwich': 5, 'Indonesian': 2, 'Singaporean': 3, 'Rajasthani': 2, 'Malaysian': 3, 'Tibetan': 1, 'Mithai': 3, 'Gujarati': 2, 'African': 1, 'Wraps': 2, 'Juices': 1, 'Bengali': 2, 'Middle Eastern': 1, 'Rolls': 4, 'Awadhi': 1, 'French': 1, 'Portuguese': 1, 'Maharashtrian': 1, 'Arabian': 2, 'Greek': 1, 'Lebanese': 1, 'Turkish': 1}


top_cuisine = [k for k, _ in sorted(popular_cuisine.items(), key=lambda item: -item[1])][:10]
top_cuisine

['North Indian',
 'Continental',
 'Chinese',
 'Asian',
 'Italian',
 'Desserts',
 'Cafe',
 'Beverages',
 'South Indian',
 'Pizza']


top_areas = top_restaurants.area.value_counts().index[:8].values
top_areas

array(['Koramangala', 'Indiranagar', 'HSR', 'Jayanagar', 'Whitefield',
       'Marathahalli', 'Kalyan Nagar', 'Sarjapur Road'], dtype=object)


df_heatmap = pd.DataFrame(dict(area=top_areas))


for cuisine in top_cuisine:
  agg = []
  for area in top_areas:
    avg_cost = top_restaurants[(top_restaurants.area == area) & (top_restaurants.cusine.str.contains(cuisine))].cost_for_two.mean()
    agg.append(avg_cost)
  df_heatmap[cuisine] = pd.Series(agg)


df_heatmap = df_heatmap.set_index('area')
df_heatmap.drop(columns=['Cafe', 'Pizza'], inplace=True)
df_heatmap.dropna(inplace=True)
df_heatmap


sns.clustermap(df_heatmap, standard_scale=1, cmap='RdBu');


df = pd.read_csv('indian_restaurants_details_cleaned_data.csv')
df.drop(labels=['zomato_url', 'telephone', 'address',  
                'online_order', 'table_reservation', 'delivery_only', 'famous_food'], inplace=True, axis=1)
df.dropna(subset=['timings'], inplace=True)


df.shape

(221556, 10)


def get_timing(city):
  sum = np.zeros(24)
  timings = df[df.city==city].timings
  for timing in timings:
    timing = eval(timing)
    for key in timing.keys():
      sum += np.array(timing[key])
  return sum / len(timings)


delhi = get_timing('Delhi NCR')
bengalore = get_timing('Bengaluru')
kolkata = get_timing('Kolkata')
mumbai = get_timing('Mumbai')


plt.plot(delhi, label='delhi')
plt.plot(bengalore, label='bengalore')
plt.plot(mumbai, label='mumbai')
plt.plot(kolkata, label='kolkata')
plt.legend();


time = []
for i in range(24):
  time.append(str(i))


import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scatterpolar(
      r=delhi,
      # fill='toself',
      name='Delhi'
))
fig.add_trace(go.Scatterpolar(
      r=bengalore,
      # fill='toself',
      name='Bengalore'
))
fig.add_trace(go.Scatterpolar(
      r=kolkata,
      # fill='toself',
      name='kolkata'
))
fig.add_trace(go.Scatterpolar(
      r=mumbai,
      # fill='toself',
      name='mumbai'
))

fig.update_layout(
  polar=dict(
    radialaxis=dict(
      visible=True,
      range=[0, 7]
    )),
  showlegend=True
)

fig.show()


df = pd.read_csv('indian_restaurants_details_cleaned_data.csv')
bangalore = get_city_restaurants(df, 'Bengaluru')


def change(x):
  if x.startswith('Koramangala'):
    return x.split(' ')[0]
  return x


bangalore.area = bangalore.area.map(change)


bangalore.area.head()

38760          Domlur
38779          Ulsoor
38801    Malleshwaram
38820         MG Road
38839        Nagawara
Name: area, dtype: object


best = bangalore[(bangalore.rating > 4.5)].sort_values('cost_for_two')


best.area.unique()

array(['Banashankari', 'Basavanagudi', 'HSR', 'Jayanagar', 'Malleshwaram',
       'Indiranagar', 'Rajajinagar', 'Kalyan Nagar',
       'Varthur Main Road, Whitefield', 'Koramangala', 'Vijay Nagar',
       'Whitefield', 'Vasanth Nagar', 'Brigade Road', 'Shanti Nagar',
       'New BEL Road', 'Marathahalli', 'Church Street',
       'Rajarajeshwari Nagar', 'BTM', 'Cunningham Road', 'Sarjapur Road',
       'Electronic City', 'JP Nagar', 'Residency Road', 'Hennur',
       'Yelahanka', 'Yeshwantpur', 'Race Course Road', 'Old Airport Road',
       'Richmond Road', 'MG Road', 'Lavelle Road'], dtype=object)


best.sort_values('rating')[:5]


collection = pd.DataFrame()
for area in bangalore.area.unique():
  best = bangalore[bangalore.area == area]
  k = best.sort_values('rating', ascending=False)[:10].sort_values('rating_count', ascending=False)[:5].sort_values('cost_for_two')[1:2]
  collection = collection.append(k)


collection.cost_for_two.mean()

678.735632183908


collection = filter_lat_long(collection)
collection


fig = px.scatter_mapbox(collection, lat="latitude", lon="longitude", hover_name="name", 
                        hover_data=["rating", "cusine", "cost_for_two", "area"],
                        size='cost_for_two', zoom=10, opacity=0.6,
                        size_max=20, color_discrete_sequence=['red'],
                        # color_continuous_scale=px.colors.sequential.Reds,
                        mapbox_style='outdoors', height=500, width=700, 
                        title='Best restaurants of the area')

fig.update_layout(margin={"r":0, "t":50, "l":0, "b":0})
fig.show()


from scipy.spatial import Voronoi, voronoi_plot_2d


vor = Voronoi(collection[['longitude', 'latitude']].values)


fig, ax = plt.subplots()
voronoi_plot_2d(vor, ax=ax)
plt.show()


import pandas as pd
import holoviews as hv
from holoviews import opts, dim
from bokeh.sampledata.les_mis import data
from bokeh.plotting import show

hv.extension('bokeh')
hv.output(size=200)


df.dropna(subset=['cusine'], inplace=True)


cuisines = []
for cuis in df.cusine:
  c = cuis.split(', ')
  cuisines.append(c)


pair_dict = {}
for sample in cuisines:
  length = len(sample)
  if length > 1:
    sample = sorted(sample)
    for i in range(length):
      for j in range(i+1, length):
        pair_dict[(sample[i], sample[j])] = pair_dict.get((sample[i], sample[j]), 0) + 1


cuisine_df = pd.DataFrame()
idx = 0
for key in pair_dict.keys():
  cuisine_df = cuisine_df.append(
      pd.DataFrame(
          {
              'source': key[0],
              'target': key[1],
              'value': pair_dict[key],
          },
          index = [idx]
      )
  )
  idx += 1


cuisine_top = cuisine_df.sort_values(by=['value'], ascending=False).iloc[:50]


cuisine_top.head()


uniques = np.unique(np.concatenate([cuisine_top.source.values, cuisine_top.target.values]))


def str_to_code(cuisine):
  return np.where(uniques==cuisine)[0][0]


cuisine_top.source = cuisine_top.source.map(str_to_code)
cuisine_top.target = cuisine_top.target.map(str_to_code)


cuisine_top.head()


node_dict = pd.DataFrame()
for i in range(len(uniques)):
  node_dict = node_dict.append(
      pd.DataFrame(
          {
              'index': i,
              'name': uniques[i]
          },
          index = [i]
      )
  )


node_dict.head()


nodes_c = hv.Dataset(node_dict, 'index')


chord = hv.Chord((cuisine_top, nodes_c))
chord.opts(
    opts.Chord(cmap='Category20', edge_cmap='Category20', labels='name',
               node_color=dim('index').str(), edge_color=dim('source').str()))
show(hv.render(chord))


df = pd.read_csv('indian_restaurants_details_cleaned_data.csv')
df.drop(labels=['zomato_url', 'telephone', 'address', 'famous_food'], inplace=True, axis=1)
df.dropna(inplace=True)


def change(x):
  if x.startswith('Koramangala'):
    return x.split(' ')[0]
  return x


bangalore = get_city_restaurants(df, 'Bengaluru')
bangalore.area = bangalore.area.map(change)
# bangalore.drop(labels=['name', 'city', 'latitude', 'longitude'], inplace=True, axis=1)
bangalore.head()


time_d = {}
for i in range(24):
  time_d['time_{:02d}'.format(i)] = 0


df_time = pd.DataFrame()
for idx, row in bangalore.iterrows():
  time_dict = time_d
  timing = eval(row.timings)
  sum = np.zeros(24)
  for key in timing.keys():
    sum += np.array(timing[key])
  for i in range(24):
    time_dict['time_{:02d}'.format(i)] = sum[i]
  df_time = df_time.append(pd.DataFrame(time_dict, index=[idx]))


bangalore.head()


df_time.head()


df_merged = bangalore.merge(df_time, how='outer', left_index=True, right_index=True)
df_merged.drop(columns=['timings'], inplace=True)
df_merged.head()


area_dummies = pd.get_dummies(df_merged['area'])


df_merged = df_merged.merge(area_dummies, how='outer', left_index=True, right_index=True)
df_merged.drop(columns=['area'], inplace=True)
df_merged.head()


popular_cuisine = {}
for cuisines in df_merged.cusine.unique():
  cuisines_list = cuisines.split(', ')
  for cuisine in cuisines_list:
    count = popular_cuisine.get(cuisine, 0)
    popular_cuisine[cuisine] = count + 1
print(popular_cuisine)

{'Cafe': 267, 'Salad': 102, 'Desserts': 263, 'Pizza': 169, 'Biryani': 447, 'Hyderabadi': 50, 'Chinese': 1048, 'Continental': 508, 'North Indian': 1242, 'Asian': 168, 'European': 75, 'Italian': 305, 'Korean': 17, 'Malaysian': 12, 'Thai': 95, 'Sushi': 13, 'Finger Food': 96, 'Seafood': 251, 'Bakery': 80, 'Afghan': 9, 'Ice Cream': 88, 'Bengali': 57, 'Momos': 89, 'Fast Food': 561, 'Mediterranean': 64, 'French': 11, 'Tea': 35, 'Mangalorean': 88, 'South Indian': 448, 'Beverages': 406, 'Rolls': 156, 'Kebab': 81, 'Kerala': 135, 'Rajasthani': 18, 'Spanish': 6, 'Burger': 168, 'BBQ': 77, 'Mughlai': 179, 'Steak': 48, 'Street Food': 193, 'Mithai': 46, 'American': 121, 'Andhra': 159, 'Juices': 92, 'Arabian': 132, 'Turkish': 13, 'Tibetan': 17, 'Nepalese': 4, 'Konkan': 10, 'Japanese': 34, 'Mexican': 55, 'Lebanese': 27, 'Vietnamese': 18, 'Chettinad': 31, 'Healthy Food': 85, 'Sandwich': 113, 'Bar Food': 19, 'Goan': 10, 'Wraps': 12, 'Odia': 23, 'Naga': 5, 'North Eastern': 6, 'Modern Indian': 25, 'Gujarati': 15, 'Kashmiri': 3, 'Bihari': 8, 'Awadhi': 6, 'Lucknowi': 9, 'Maharashtrian': 17, 'Indonesian': 8, 'Coffee': 21, 'Paan': 2, 'Middle Eastern': 12, 'Drinks Only': 2, 'Sri Lankan': 2, 'Iranian': 3, 'German': 2, 'Charcoal Chicken': 6, 'Australian': 1, 'Greek': 3, 'Roast Chicken': 1, 'Singaporean': 4, 'Bubble Tea': 3, 'Raw Meats': 2, 'Hot dogs': 2, 'Bohri': 1, 'Assamese': 4, 'Belgian': 3, 'Tamil': 4, 'Grill': 1, 'Tex-Mex': 6, 'Frozen Yogurt': 1, 'Parsi': 3, 'Mongolian': 1, 'Burmese': 8, 'Cantonese': 2, 'Portuguese': 1, 'British': 1, 'Sindhi': 1, 'African': 1}


cuisines = list(popular_cuisine.keys())


df_cuisine = pd.DataFrame()
for idx, row in df_merged.iterrows():
  for i in cuisines:
    cuisine_dict[i] = 0
  c_list = row.cusine.split(', ')
  for c in c_list:
    cuisine_dict[c] = 1
  df_cuisine = df_cuisine.append(pd.DataFrame(cuisine_dict, index=[idx]))


df_cuisine.tail()


df_merged = df_merged.merge(df_cuisine, how='outer', left_index=True, right_index=True)
df_merged.drop(columns=['cusine'], inplace=True)
df_merged.head()


# df_merged.to_csv('tsne_bangalore.csv', index=False)
df_merged = pd.read_csv('tsne_bangalore.csv')


df_merged[df_merged.columns[30:117]]


cols_to_remove = list(df_merged.columns[30:117])
cols_to_remove.remove('Koramangala')


df_merged_kor = df_merged.drop(columns=cols_to_remove)
df_merged_kor = df_merged_kor[df_merged_kor['Koramangala']==1]
df_merged_kor.drop(columns=['Koramangala'], inplace=True)
df_merged_kor.head()


df_norm = (df_merged_kor - df_merged_kor.mean()) / df_merged_kor.std()
df_norm.dropna(axis=1, inplace=True)
df_norm.head()


from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, perplexity=100)
k = tsne.fit_transform(df_norm)


k.shape

(834, 2)


bangalore = get_city_restaurants(df, 'Bengaluru')
bangalore.head()


bangalore_tsne = bangalore.iloc[df_norm.index]
bangalore_tsne['tsne_0'] = k[:, 0]
bangalore_tsne['tsne_1'] = k[:, 1]
bangalore_tsne.dropna(inplace=True)


fig = px.scatter(bangalore_tsne, x="tsne_0", y="tsne_1", color='rating', size="cost_for_two" , size_max=25, 
                  hover_data=['name', 'rating', 'cost_for_two', 'cusine'],)
fig.update_layout(title_text='T-SNE for Koramangala area',
                  height=700, width=1000)
fig.show()


import matplotlib.image as image
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from matplotlib.lines import Line2D
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from scipy import stats


plt.rcParams.update({"font.family": "Reem Kufi"})


def adjust_lightness(color, amount=0.5):
    import matplotlib.colors as mc
    import colorsys
    try:
        c = mc.cnames[color]
    except:
        c = color
    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
    return colorsys.hls_to_rgb(c[0], c[1] * amount, c[2])


df_char_vis = pd.read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-30/character_visualization.csv"
)


df_best_chars = pd.DataFrame({
    "rank": np.linspace(1, 10, num=10),
    "char_popular": ["Wolverine", "Magneto", "Nightcrawler", "Gambit",
                     "Storm", "Colossus", "Phoenix", "Professor X", 
                     "Iceman", "Rogue"]
})


for character in df_best_chars["char_popular"]:
    idxs = df_char_vis["character"].str.contains(character)
    df_char_vis.loc[idxs, "character_join"] = character


df_best_stream = (
    pd.merge(df_char_vis, df_best_chars, left_on="character_join", right_on="char_popular")
    .groupby(["character", "char_popular", "costume", "rank", "issue"]).agg(
        speech = ("speech", sum),
        thought = ("thought", sum),
        narrative = ("narrative", sum),
        depicted = ("depicted", sum),
    )
    .query("rank <= 5")   # keep only the top 5 characters
    .query("issue < 281") 
    .reset_index()
)


# Costume is either 'customed' or 'casual'
df_best_stream["costume"] = np.where(df_best_stream["costume"] == "Costume", "costumed", "casual")

# char_costume contains the name of the character and the costume
df_best_stream["char_costume"] = df_best_stream["char_popular"] + " (" + df_best_stream["costume"] + ")"

# Record the categories of 'char_costume'. 
# This will be used for the order of the areas in the streamchart.
CATEGORIES = df_best_stream.sort_values(by=["rank", "char_costume"])["char_costume"].unique()
CATEGORIES = CATEGORIES[::-1]

# Put the data in long form
df_best_stream = pd.melt(
    df_best_stream,
    id_vars = ["character", "char_popular", "costume", "rank", "issue", "char_costume"],
    value_vars = ["speech", "thought", "narrative", "depicted"],
    var_name = "parameter",
    value_name = "value"
)


df_best_stream = df_best_stream.sort_values(by = ["char_costume", "issue"])
df_best_stream = df_best_stream.groupby(["char_costume", "issue", "parameter"]).agg(
    value = ("value", np.mean)
).reset_index()


PALETTE = [
    adjust_lightness("#595A52", 1.25), "#595A52",
    adjust_lightness("#8E038E", 1.2), "#8E038E",
    adjust_lightness("#13AFEF", 1.25), "#13AFEF",
    adjust_lightness("#C20008", 1.2), "#C20008",
    adjust_lightness("#FFB400", 1.25), "#FFB400"
]

GREY25 = "#404040"
GREY30 = "#4d4d4d"
GREY40 = "#666666"
GREY88 = "#e0e0e0"
GREY95 = "#f2f2f2"

# IMAGE = image.imread("uncannyxmen.png")

XTICKS = [97, 125, 150, 175, 200, 225, 250, 280]


def gaussian_smooth(x, y, grid, sd):
    weights = np.transpose([stats.norm.pdf(grid, m, sd) for m in x])
    weights = weights / weights.sum(0)
    return (weights * y).sum(1)


df_depicted = df_best_stream.query("parameter == 'depicted'")

issues = [
    df_depicted[df_depicted["char_costume"] == character]["issue"].values
    for character in CATEGORIES
]
values = [
    df_depicted[df_depicted["char_costume"] == character]["value"].values
    for character in CATEGORIES
]

grid = np.linspace(80, 300, num=1000)


# Basic stacked area chart.
fig, ax = plt.subplots(figsize=(10, 7))

# sd=2 is the standard deviation of the Gaussian function.
values_smoothed = [gaussian_smooth(x, y, grid, sd=2) for x, y in zip(issues, values)]
ax.stackplot(grid, values_smoothed, colors=PALETTE, baseline="sym");

findfont: Font family ['Reem Kufi'] not found. Falling back to DejaVu Sans.


# Set background color
ax.set_facecolor(GREY88)

# This 'line' is the sum of values for each issue.
line = np.array(values_smoothed).sum(0)
# Two lines are added, one on top, another on the bottom.
# Both have the same height because of `baseline="sym"`
ax.plot(grid, line / 2, lw=1.5, color="white")
ax.plot(grid, -line / 2, lw=1.5, color="white")
fig


def streamgraph(df, parameter, ax, grid, sd=2):
    # Keep rows for the given 'parameter'
    df = df[df["parameter"] == parameter]
    
    # Same logic than above
    issues = [
        df[df["char_costume"] == character]["issue"].values
        for character in CATEGORIES
    ]
    values = [
        df[df["char_costume"] == character]["value"].values
        for character in CATEGORIES
    ]
    # Smooth values
    values_smoothed = [gaussian_smooth(x, y, grid, sd) for x, y in zip(issues, values)]
    
    # Add streamchart
    ax.stackplot(grid, values_smoothed, colors=PALETTE, baseline="sym")
    
    # Add border lines
    line = np.array(values_smoothed).sum(0)
    ax.plot(grid, line / 2, lw=1.5, color="white")
    ax.plot(grid, -line / 2, lw=1.5, color="white")
    
    # Vertical lines
    for x in XTICKS:
        ax.axvline(x, color=GREY88, ls=(0, (1, 2)), zorder=10)
    
    # Change background color and remove both axis
    ax.set_facecolor(GREY88)
    ax.yaxis.set_visible(False)
    ax.xaxis.set_visible(False)
    
    # Also remove all spines
    ax.spines["left"].set_color("none")
    ax.spines["bottom"].set_color("none")
    ax.spines["right"].set_color("none")
    ax.spines["top"].set_color("none")


# Some layout stuff ----------------------------------------------
# sharex=True ensures each panel has the same horizontal range
fig, ax = plt.subplots(4, 1, figsize=(14, 10.5), sharex=True)

# Background color for the figure (not each axis)
fig.patch.set_facecolor(GREY88)

# Adjust space between panels
fig.subplots_adjust(left=0.01, bottom=0.1, right=0.99, top=0.9, hspace=0.05)

# Add streamcharts -----------------------------------------------
# This loops along the four axes in the figure.
grid = np.linspace(80, 300, num=1000)
for idx, parameter in enumerate(["depicted", "speech", "thought", "narrative"]):
    streamgraph(df_best_stream, parameter, ax[idx], grid)


# Add label for horizontal axis ----------------------------------
# Note this is only modifying the labels for `ax[0]`, the top panel.

ax[0].xaxis.set_visible(True)
ax[0].tick_params(axis="x", labeltop=True, length=0)
ax[0].set_xticks(XTICKS)
ax[0].set_xticklabels([f"Issue\n#{x}" for x in XTICKS], color=GREY30);


# Add labels for each panel axis ---------------------------------
# These labels indicate which type of appearence is represented
# on each panel.

levels = ["depicted", "speech", "thought", "narrative"]
labels = pd.DataFrame({
    "issue": [78] * 4,
    "value": [-21, -19, -14, -11],
    "parameter": pd.Categorical(levels, levels),
    "label": ["Depicted", "Speech\nBubbles", "Thought\nBubbles", "Narrative\nStatements"]
})

for idx, row in labels.iterrows():
    ax[idx].text(
        0.08, 
        0.3, 
        row["label"], 
        ha="center",
        va="center",
        ma="left",
        color=GREY25,
        size=14,
        weight=900,
        transform=ax[idx].transAxes,

    )
fig

findfont: Font family ['Reem Kufi'] not found. Falling back to DejaVu Sans.


# Add informative text -------------------------------------------

# The dictionaries in TEXTS contain all the information needed
# to add all the text blocks: the text, the axis where
# the text is placed, the xy location, and the color.


TEXTS = [
    {
        "text": 'Gambit was introduced for the\nfirst time in issue #266 called\n"Gambit: Out of the Frying\nPan"— nevertheless, he is the\n4th most popular X-Men\ncharacter!',
        "ax": 0,
        "x": 0.92,
        "y": 0.1,
        "color": adjust_lightness("#8E038E", 1.05)
    },
    {
        "text": 'Wolverine is the most popular\nX-Men and has a regular\npresence in the X-Men comics\nbetween 1975 and 1991',
        "ax": 0,
        "x": 0.06,
        "y": 0.80,
        "color": adjust_lightness("#FFB400", 1.1)
    },
    {
        "text": 'Storm is by far the most\nthoughtful of the five most\npopular X-Men characters,\n especially in issues #220, #223\nand #265. Storm ranks 5th.',
        "ax": 2,
        "x": 0.725,
        "y": 0.875,
        "color": adjust_lightness("#595A52", 1.01)
    },
    {
        "text": "Magneto was ranked by IGN\nas the Greatest Comic Book\nVillain of All Time. And even\nthough he only appears from\ntime to time he ranks 2nd-\n4 ranks higher than his friend\nand opponent Professor X!",
        "ax": 1,
        "x": 0.225,
        "y": 1.02,
        "color": adjust_lightness("#C20008", 1.05)
    },
    {
        "text": 'The 3rd most popular X-men\ncharacter Nightcrawler gets\ninjured during the "Mutant\nMassacre" and fell into a coma\nafter an attack from Riptide in\nissue #211.',
        "ax": 1,
        "x": 0.5,
        "y": 1.02,
        "color": adjust_lightness("#13AFEF", 1.1)
    },
    
]

for d in TEXTS:
    ax[d["ax"]].text(
        x = d["x"],
        y = d["y"],
        s = d["text"], 
        ha="center",
        va="center",
        ma="left",
        fontsize=7.5,
        color=d["color"],
        bbox=dict(
            boxstyle="round", 
            facecolor=GREY95,
            edgecolor=d["color"],
            pad=0.6
        ),
        # This transform means we pass (0, 1) coordinates to locate
        # the text block
        transform=ax[d["ax"]].transAxes,
        zorder=999
    )
    # This ensures the text is on top of everything
    fig.texts.append(ax[d["ax"]].texts.pop())
fig

findfont: Font family ['Reem Kufi'] not found. Falling back to DejaVu Sans.


# Add legend -----------------------------------------------------

# A helper function that creates each handle for the legend
def get_handle(label, color):
    line = Line2D(
        [0], 
        [0], 
        color=color, 
        label=label,
        lw=8
    )
    return line

# Create the labels
names = ["Wolverine", "Magneto", "Nightcrawler", "Gambit", "Storm"]
costumes = ["casual", "costumed"]
labels = [f"{name} ({costume})" for name in names for costume in costumes]

# And create the handles
handles = [get_handle(label, color) for label, color in zip(labels, PALETTE[::-1])]

# Now, add the legend. 
legend = fig.legend(
    handles=handles,
    bbox_to_anchor=[0.5, 0.07], # Located in the mid-bottom of the figure.
    edgecolor=GREY40,
    labelspacing=-0.1,
    loc="center", 
    ncol=5 # The 10 handles are splitted between 5 columns
)

# Change size and color of legend labels
for text in legend.get_texts():
    text.set_fontsize(8)   
    text.set_color(GREY40)

# And finally give a rounded appearence to the frame of the legend
legend.get_frame().set_boxstyle("round", rounding_size=0.4, pad=0.1)    


# Add title ------------------------------------------------------
# Note the space in the text. It is where the image will be located.
fig.text(
    0.5,
    0.95,
    "Appearance of the Five Most Popular X-Men Characters in Chris Claremont's                           Comics",
    fontsize=24,
    fontweight="bold",
    ha="center"
)


# Create annotation box to place image.
# It will be added at (0.815, 0.955) in figure coordinates.
# (0, 0) is bottom-left and (1, 1) is top-right.
# ab = AnnotationBbox(
#     OffsetImage(IMAGE, zoom=0.20), # Add the image with a 20% of its original size.
#     (0.815, 0.955), 
#     xycoords="figure fraction", 
#     box_alignment=(0, 0.5),
#     pad=0, 
#     frameon=False
# )

# Add the annotation box into the figure
# fig.add_artist(ab)


# Add caption ----------------------------------------------------
# And finally, the caption that gives credit to the creator of 
# this amazing viz.
fig.text(
    0.5, 
    0.02, 
    "Visualization by Cédric Scherer  •  Data by Claremont Run Project via Malcom Barret  •  Popularity Scores by ranker.com  •  Logo by Comicraft",
    color=GREY40,
    fontsize=8,
    ha="center"

)

# Note: you can use `fig.savefig("plot.png", dpi=300)` to see this with better quality.
fig

findfont: Font family ['Reem Kufi'] not found. Falling back to DejaVu Sans.
findfont: Font family ['Reem Kufi'] not found. Falling back to DejaVu Sans.

	zomato_url	name	city	area	rating	rating_count	telephone	cusine	cost_for_two	address	timings	online_order	table_reservation	delivery_only	famous_food	longitude	latitude
0	https://www.zomato.com/ncr/sainik-food-pandav-...	Sainik Food	Delhi NCR	Pandav Nagar	3.2	21.0	011 22486474 +91 9717806814	North Indian	300.0	C 4/1, Opposite Mother Dairy, Pandav Nagar, Ne...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	False	False	False	NaN	77.284871	28.617732
1	https://www.zomato.com/mumbai/kunals-creamery-...	Kunal's Creamery & Eatery	Mumbai	Ambernath	3.6	51.0	+91 9561356690 +91 9637537499	Street Food, Chinese, Fast Food	500.0	Shop 14, Trishul Shivdham Complex, Shiv Mandir...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1...	False	False	False	Penne Pasta	73.184287	19.205887
2	https://www.zomato.com/ncr/brij-palace-restaur...	Brij Palace Restaurant	Delhi NCR	Jasola	NaN	NaN	+91 9891828106	North Indian	250.0	4, Okhla Bus Stand, Jamia Nagar, Near, Jasola,...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	False	False	False	Chana Masala , Butter Naan , Paneer Bhujia , D...	77.291223	28.563034

	name	city	area	rating	rating_count	cusine	cost_for_two	timings	famous_food	longitude	latitude
1	Kunal's Creamery & Eatery	Mumbai	Ambernath	3.6	51.0	Street Food, Chinese, Fast Food	500.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1...	Penne Pasta	73.184287	19.205887
7	Applemint	Mumbai	Kharghar	3.5	94.0	Chinese	200.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Momos , Liti Chokha , Chicken Momo , Litti , N...	73.065527	19.033390
9	Food Fantasy	Delhi NCR	Mayur Vihar Phase 1	3.8	437.0	North Indian, Chinese, Fast Food	500.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1...	Tandoori Chicken , Pizza , Dal Makhani , Butte...	77.292763	28.608202
10	Curry And Combos Twist	Mumbai	Goregaon East	4.4	92.0	North Indian, Chinese, Fast Food, Pizza, Panin...	550.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 1,...	Kadhai Paneer , Paneer Tikka , Gulab Jamun , C...	72.869879	19.175588
15	Food Village	Kolkata	Baranagar	2.8	355.0	North Indian	650.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Tandoori Chicken , Fish , Bbq Chicken , Veg Pu...	88.377416	22.652427

	rating	rating_count	cost_for_two	longitude	latitude
count	10874.000000	10874.000000	10874.000000	10874.000000	10874.000000
mean	3.777405	324.633254	672.450616	77.192627	28.577068
std	0.380904	558.488792	605.794935	0.103761	0.080266
min	1.800000	4.000000	50.000000	76.980779	28.403493
25%	3.600000	75.000000	350.000000	77.097332	28.525841
50%	3.800000	153.000000	500.000000	77.193825	28.573724
75%	4.000000	345.000000	700.000000	77.268342	28.640872
max	4.900000	10035.000000	8000.000000	77.430198	28.743949

	name	city	area	rating	rating_count	cost_for_two	longitude	latitude	cuisine	weight
0	AB's - Absolute Barbecues	Bengaluru	BTM	4.9	7363.0	1600.0	77.604780	12.916358	European	0.250000
1	AB's - Absolute Barbecues	Bengaluru	BTM	4.9	7363.0	1600.0	77.604780	12.916358	Mediterranean	0.250000
2	AB's - Absolute Barbecues	Bengaluru	BTM	4.9	7363.0	1600.0	77.604780	12.916358	North Indian	0.250000
3	AB's - Absolute Barbecues	Bengaluru	BTM	4.9	7363.0	1600.0	77.604780	12.916358	BBQ	0.250000
4	Byg Brewski Brewing Company	Bengaluru	Sarjapur Road	4.9	19305.0	1600.0	77.682906	12.912934	Continental	0.166667

	name	city	area	rating	rating_count	cost_for_two	longitude	latitude	cuisine	weight
73	ECHOES Koramangala	Bengaluru	Koramangala	4.7	3529.0	950.0	77.615752	12.934126	Desserts	0.25
98	Lot Like Crepes	Bengaluru	Koramangala	4.7	1051.0	550.0	77.613468	12.936585	Desserts	0.30
105	Corner House Ice Cream	Bengaluru	Koramangala	4.7	380.0	350.0	77.619799	12.933721	Desserts	0.30
166	XO Belgian Waffle	Bengaluru	Koramangala	4.6	920.0	500.0	77.616048	12.934448	Desserts	0.30
237	Belgian Waffle Factory	Bengaluru	Koramangala	4.6	322.0	500.0	77.616924	12.934560	Desserts	0.60
242	The Pancake Story	Bengaluru	Koramangala	4.6	175.0	450.0	77.633492	12.925924	Desserts	0.30

Maps¶

Which is the best neighborhood in my city?¶

Best localities if I want to keep my bill low¶

Popular cuisine in my neighbourhood¶

Is paying more for food really worth?¶

How does the average ratings of different cities compare¶

Cost of food across cities¶

Operation time of restaurants¶

Voronoi for best restaurant per area¶

How frequently are these cuisines served together?¶

T-SNE to cluster restaurants¶

TSNE within area¶

Random but cool stuff¶

	area	cuisine	counts	cost_for_two
0	BTM	Asian	1	1300.0
1	BTM	BBQ	1	1600.0
2	BTM	Bar Food	1	2000.0
3	BTM	Chinese	1	2000.0
4	BTM	Continental	2	1650.0

	name	city	area	rating	rating_count	cusine	cost_for_two	timings	famous_food	longitude	latitude
65428	Milano Ice Cream	Bengaluru	Indiranagar	4.9	2370.0	Ice Cream, Desserts	400.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1...	Truffle , Tiramisu , Apple Pie , Carrot Cake ,...	77.644039	12.979121
64788	Taaza Thindi	Bengaluru	Jayanagar	4.9	4376.0	South Indian	100.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0...	Masala Dosa , Idli , Vada , Kesari Bath , Filt...	77.593459	12.922829
70248	Brahmin's Coffee Bar	Bengaluru	Basavanagudi	4.8	2812.0	South Indian	100.0	{'Mon': [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0...	Filtered Coffee , Kesari Bath , Idli Vada , Up...	77.568948	12.954032
67730	Arogya Ahaara	Bengaluru	HSR	4.7	570.0	South Indian	100.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1...	Coffee , Idli , Masala Dosa , Vada , Kesari Ba...	77.638023	12.918325
71009	O.G. Variar & Sons	Bengaluru	Rajajinagar	4.7	1256.0	Bakery, Desserts	200.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1...	Veg Puff , Plum Cake , Sponge Cake , Coconut B...	77.554538	12.991436

	name	city	area	rating	rating_count	cusine	cost_for_two	timings	famous_food	longitude	latitude
188266	Mainland China	Bengaluru	Whitefield	4.8	2229.0	Chinese, Asian, Sushi, Japanese, Thai	1700.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Noodle , Dumplings , Fish , Tai Pai Chicken , ...	77.725158	12.955788
62600	Biergarten	Bengaluru	Whitefield	4.7	8263.0	Continental, European, BBQ, Chinese, Asian	2400.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Wheat Beer , Pizza , Nachos , Cocktails , Riso...	77.708533	12.982446
63621	Windmills Craftworks	Bengaluru	Whitefield	4.7	6289.0	American, North Indian, Salad	2500.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Beer , Cocktails , Hefeweizen , Keema Pav , Na...	77.721979	12.982413
184567	AB's - Absolute Barbecues	Bengaluru	Whitefield	4.7	4215.0	European, Mediterranean, North Indian, BBQ	1600.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Paan Ice Cream , Fish , Sea Food , Prawn , Cri...	77.724956	12.976904
187366	Polar Bear	Bengaluru	Varthur Main Road, Whitefield	4.6	114.0	Desserts, Ice Cream, Bakery, Fast Food	300.0	{'Mon': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Brownie , Hot Chocolate Fudge , Death By Choco...	77.721558	12.956148

	name	city	area	rating	rating_count	cusine	cost_for_two	timings	famous_food	longitude	latitude
38118	The Turnover By Santa Delivers	Kolkata	Sector 5, Salt Lake	4.2	51.0	North Indian	350.0	{'Mon': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1...	Butter Chicken , Cheesy Chicken Kebab , Shredd...	88.435204	22.584398
44211	Honest	Mumbai	Borivali West	3.6	168.0	North Indian, South Indian, Fast Food	550.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5,...	Pav Bhaji , Pasta , Tawa Pulav , Panipuri , Pi...	72.852172	19.227350
180831	FreshMenu	Mumbai	Andheri East	3.7	202.0	Continental, Healthy Food, Chinese, North Indi...	450.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1...	Noodle , Chicken Steak , Spinach Corn Sandwich...	72.859926	19.119136
33527	Dakshin	Mumbai	Borivali West	3.6	453.0	Chinese, North Indian, Malwani, Seafood	900.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1...	Sea Food , Sol Kadhi , Malwani Food , Roti , C...	72.842721	19.229620
7287	Pt.Kanhaiyalal & Durga Prasad Dixit Paranthe Wale	Delhi NCR	Chandni Chowk	3.9	417.0	North Indian	150.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1...	Papad Paratha , Sweet Lassi , Aloo Paratha , S...	77.230427	28.656073

	North Indian	Chinese	Fast Food	Continental	Italian	Beverages	South Indian	Biryani	Desserts	Cafe
city
Delhi NCR	742.353445	724.053012	472.241709	1236.777669	1149.141414	489.662822	542.212042	612.016807	491.753821	761.764706
Mumbai	817.488473	767.589528	507.271723	1316.035354	1179.783784	530.738994	571.498906	622.146119	486.994697	708.030303
Bengaluru	716.232048	670.067323	475.718016	1136.204380	1055.974843	474.058577	543.013699	573.519164	482.435897	669.287212
Pune	809.783433	786.382979	505.027174	1261.556604	1048.026316	522.558140	531.518625	598.721591	498.654244	588.817891
Kolkata	688.383751	636.334845	415.571635	1047.734139	956.949807	474.204947	471.683938	525.724638	464.035088	659.042553
Hyderabad	746.554904	723.624628	472.879684	1193.400000	1158.734940	427.192982	625.345745	624.570815	441.379310	711.864407
Chennai	778.940678	691.448008	459.983766	1294.982699	1071.245421	491.528239	648.463855	613.711584	524.063401	680.635838
Ahmedabad	734.819897	682.440476	504.198473	1042.783505	853.481013	474.774775	539.383562	610.000000	454.934211	661.486486
Jaipur	882.838915	848.534031	541.292135	1347.735849	1101.584699	426.562500	582.812500	500.000000	424.242424	658.928571
Chandigarh	744.425957	727.622378	475.268817	1304.166667	1123.831776	474.137931	503.977273	575.362319	502.912621	774.285714

	name	city	area	rating	rating_count	cusine	cost_for_two	timings	famous_food	longitude	latitude
38779	Cafe Kaara	Bengaluru	Ulsoor	4.3	188.0	Cafe, Salad, Desserts, Pizza	750.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 1,...	Coffee , Hot Chocolate , Pasta , Risotto , Tir...	77.617962	12.975299
38801	Hyderabad Biryani House	Bengaluru	Malleshwaram	4.2	291.0	Biryani, Hyderabadi, Chinese	800.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5,...	Hyderabadi Biryani , Mutton Biryani , Chicken ...	77.571417	12.994095
38820	The Coffee Bean & Tea Leaf	Bengaluru	MG Road	4.0	308.0	Cafe	700.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1,...	Coffee , Sandwich , Blueberry Muffin , Cappucc...	77.620303	12.973778
38839	Fly Dining	Bengaluru	Nagawara	3.8	137.0	Continental, North Indian	14000.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	Pasta , Bruschettas	77.610328	13.047513
38859	Pot Biryani	Bengaluru	Domlur	3.8	497.0	Biryani, North Indian, Chinese	650.0	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5,...	Pot Biryani , Boneless Biryani , Raita , Panee...	77.641729	12.954721

	North Indian	Continental	Chinese	Asian	Italian	Desserts	Beverages	South Indian
area
Koramangala	1198.437500	1178.846154	1230.555556	1407.142857	1246.153846	540.909091	690.000000	870.0
Indiranagar	1153.333333	1286.666667	1244.444444	1554.545455	1383.333333	642.857143	594.444444	1020.0
HSR	1021.428571	1295.000000	1000.000000	1433.333333	1400.000000	457.142857	662.500000	470.0
Jayanagar	783.333333	900.000000	800.000000	1100.000000	1033.333333	650.000000	450.000000	280.0
Whitefield	1780.000000	1883.333333	2020.000000	1844.444444	1675.000000	800.000000	766.666667	1900.0
Marathahalli	1500.000000	1600.000000	1783.333333	1433.333333	1200.000000	687.500000	500.000000	2700.0
Sarjapur Road	1575.000000	1562.500000	1250.000000	1425.000000	1550.000000	1500.000000	200.000000	1550.0

	zomato_url	name	city	area	rating	rating_count	telephone	cusine	cost_for_two	address	timings	online_order	table_reservation	delivery_only	famous_food	longitude	latitude
66619	https://www.zomato.com/bangalore/output-bengal...	Output Bengaluru	Bengaluru	Marathahalli	4.6	1384.0	080 49653064	North Indian, Continental	1500.0	Shop 36/4, 4th Floor, Soul Space Arena, Outer ...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	False	True	False	Cocktails , Craft Beer , Pasta , Bbq Chicken P...	77.693371	12.980166
75354	https://www.zomato.com/bangalore/maziga-whitef...	Maziga	Bengaluru	Whitefield	4.6	473.0	080 49652198	Modern Indian	1500.0	3rd Floor, VR Mall, B Narayanapura, Mahadevapu...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	True	True	False	Biryani , Mocktails , Dal Makhani , Fish , Ris...	77.695240	12.996256
63522	https://www.zomato.com/bangalore/tipsy-bull-th...	Tipsy Bull - The Bar Exchange	Bengaluru	JP Nagar	4.6	550.0	080 49653138	Chinese, Continental, North Indian, Mexican	1500.0	603, 15th Cross, 100 Feet Ring Road, 6th Phase...	{'Mon': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	False	True	False	Cocktails , Nachos , Pizza , Fries , Chicken W...	77.577558	12.906472
62719	https://www.zomato.com/koramangalasocial	Koramangala Social	Bengaluru	Koramangala	4.6	5212.0	080 43723295 080 43723095	American, North Indian, Chinese, Asian, Biryan...	1500.0	118, Koramangala Industrial Area, Koramangala ...	{'Mon': [0.5, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,...	True	False	False	Cocktails , Long Island Iced Tea , Nachos , Pa...	77.614284	12.935441
62660	https://www.zomato.com/bangalore/the-pump-hous...	The Pump House	Bengaluru	JP Nagar	4.6	1506.0	+91 9606127666 +91 9606129666	Continental, Finger Food	1500.0	607, 15th Cross, Outer Ring Road, 1st Phase, J...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	False	True	False	Beer , Cocktails , Pizza , Nachos , Fries , St...	77.582139	12.906545

	zomato_url	name	city	area	rating	rating_count	telephone	cusine	cost_for_two	address	timings	online_order	table_reservation	delivery_only	famous_food	longitude	latitude
75015	https://www.zomato.com/bangalore/murphys-brewh...	Murphy's Brewhouse - The Paul Bangalore	Bengaluru	Domlur	4.3	2086.0	080 40477755 080 40477777	Continental, North Indian, Finger Food	1700.0	The Paul, 139/28, Off Intermediate Ring Road, ...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1...	False	True	False	Chicken Bruschetta , Wheat Beer , Tandoori Chi...	77.639900	12.953824
45975	https://www.zomato.com/bangalore/marzipan-cafe...	Marzipan Cafe & Bakery	Bengaluru	Ulsoor	4.3	477.0	+91 9844422724 +91 9902069665	Cafe, Mediterranean, Bakery, Greek, Beverages	900.0	22, Ulsoor Road, (ground floor of Sunrise Cham...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1...	False	False	False	Coffee , Cheesecake , Pasta , Hot Chocolate , ...	77.619141	12.974795
67248	https://www.zomato.com/bangalore/veena-stores-...	Veena Stores	Bengaluru	Malleshwaram	4.5	2547.0	080 23344838	South Indian	150.0	187, 15th Cross, Margosa Road, Malleshwaram, B...	{'Mon': [0, 0, 0, 0, 0, 0, 0.5, 1, 1, 1, 1, 1,...	True	False	False	Kesari Bath , Idli Vada , Sweet Pongal , Filte...	77.569212	13.005523
68509	https://www.zomato.com/bangalore/portland-stea...	Portland Steakhouse & Cafe	Bengaluru	MG Road	4.3	2137.0	080 25593405 080 41127142	Steak, Italian, Continental, American, Burger,...	1500.0	1, Brunton Road, 1st Cross, Ashok Nagar, MG Ro...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5,...	True	True	False	Beef Steak , Burgers , Apple Pie , Spaghetti ,...	77.614819	12.970258
67184	https://www.zomato.com/bangalore/aromas-hydera...	Aroma's Hyderabad House	Bengaluru	Nagawara	4.2	733.0	+91 8970588888 +91 8970688888	Mughlai, Biryani, North Indian, Chinese	800.0	Opposite Manyatha Tech Park, Beside Honda Serv...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75...	True	False	False	Vegetable Biryani , Hyderabadi Biryani , Raita...	77.619832	13.040641
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
181610	https://www.zomato.com/bangalore/barista-1-lan...	Barista	Bengaluru	Langford Town	NaN	NaN	+91 7022375531	Cafe, Coffee, Fast Food, Beverages, Desserts	300.0	4, Raksha Complex, O'Shaughnessy Road, Langfor...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1...	True	False	False	NaN	77.599529	12.959872
182953	https://www.zomato.com/bangalore/pulimunchi-sa...	Pulimunchi	Bengaluru	Sadashiv Nagar	4.1	550.0	080 23616678 +91 9741310329	Mangalorean, Seafood	1000.0	188/1, 1st Floor, Upper Palace Orchards, Sadas...	{'Mon': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...	True	True	False	Neer Dosa , Tender Coconut Souffle , Sea Food ...	77.579054	13.006639
46811	https://www.zomato.com/bangalore/hushys-kitche...	Hushy's Kitchen	Bengaluru	Richmond Town	3.6	74.0	080 43007100 +91 9343361684	Fast Food, Chinese, Burger	300.0	Rhenius Road, Opposite Hockey Stadium, Richmon...	{'Mon': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	True	False	False	Burgers , Biryani , Chicken Burger , Rolls , V...	77.601810	12.961361
60697	https://www.zomato.com/bangalore/the-raj-pavil...	The Raj Pavilion - ITC Windsor	Bengaluru	Sankey Road	4.2	400.0	080 61401224	North Indian, Continental, Asian	2000.0	ITC Windsor, 25 Windsor Square, Sankey Road, B...	{'Mon': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...	False	True	False	Pizza , Hot Chocolate , Salad , Creme Brulee ,...	77.585229	12.994645
181968	https://www.zomato.com/bangalore/ice-corner-sh...	Ice Corner	Bengaluru	Central Bangalore	3.6	NaN	080 40414141	Ice Cream, Desserts, Salad, Beverages	150.0	NaN	{'Mon': [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1...	True	False	False	NaN	77.613979	12.999681

	source	target	value
6	Chinese	North Indian	42533
24	North Indian	South Indian	12570
12	Fast Food	North Indian	11901
16	Chinese	South Indian	10986
0	Chinese	Fast Food	10823

	time_09	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23
38779	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0	0.0
38801	0.0	0.0	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	3.5
38820	0.0	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0
38839	0.0	0.0	0.0	0.0	0.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0
38859	0.0	0.0	3.5	7.0	7.0	7.0	4.0	1.0	1.0	4.0	7.0	7.0	7.0	7.0	0.0

	area	rating	rating_count	cusine	cost_for_two	online_order	table_reservation	delivery_only	time_09	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23
38779	Ulsoor	4.3	188.0	Cafe, Salad, Desserts, Pizza	750.0	False	False	False	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0	0.0
38801	Malleshwaram	4.2	291.0	Biryani, Hyderabadi, Chinese	800.0	False	False	False	0.0	0.0	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	3.5
38820	MG Road	4.0	308.0	Cafe	700.0	True	False	False	0.0	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0
38839	Nagawara	3.8	137.0	Continental, North Indian	14000.0	False	False	False	0.0	0.0	0.0	0.0	0.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0
38859	Domlur	3.8	497.0	Biryani, North Indian, Chinese	650.0	True	False	False	0.0	0.0	3.5	7.0	7.0	7.0	4.0	1.0	1.0	4.0	7.0	7.0	7.0	7.0	0.0

	rating	rating_count	cusine	cost_for_two	online_order	table_reservation	delivery_only	time_09	time_10	time_11	time_12	time_13	time_14	time_15	time_16	time_17	time_18	time_19	time_20	time_21	time_22	time_23	...	Malleshwaram	Nagawara	Ulsoor
38779	4.3	188.0	Cafe, Salad, Desserts, Pizza	750.0	False	False	False	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0	0.0	...	0	0	1
38801	4.2	291.0	Biryani, Hyderabadi, Chinese	800.0	False	False	False	0.0	0.0	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	3.5	...	1	0	0
38820	4.0	308.0	Cafe	700.0	True	False	False	0.0	3.5	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0	...	0	0	0
38839	3.8	137.0	Continental, North Indian	14000.0	False	False	False	0.0	0.0	0.0	0.0	0.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	7.0	0.0	...	0	1	0
38859	3.8	497.0	Biryani, North Indian, Chinese	650.0	True	False	False	0.0	0.0	3.5	7.0	7.0	7.0	4.0	1.0	1.0	4.0	7.0	7.0	7.0	7.0	0.0	...	0	0	0

	Chinese	North Indian	Thai	Seafood	Momos	Mangalorean	...
190148	0	1	0	0	0	0	...
190158	0	1	0	0	0	0	...
190175	0	1	0	0	0	0	...
190193	1	0	1	0	1	0	...
190213	0	0	0	1	0	1	...

	BTM	Banashankari	Banaswadi	Bannerghatta Road	Basavanagudi	Basaveshwara Nagar	Bellandur	Bommanahalli	Brigade Road	Brookefield	...	Ulsoor	Uttarahalli	Varthur Main Road, Whitefield	Vasanth Nagar	Vijay Nagar	West Bangalore	Whitefield	Wilson Garden	Yelahanka	Yeshwantpur
0	0	0	0	0	0	0	0	0	0	0	...	1	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13087	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
13088	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
13089	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
13090	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
13091	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

	rating	rating_count	cost_for_two	online_order	table_reservation	delivery_only	time_00	time_01	...
27	3.8	29.0	500.0	True	False	False	7.00	7.0	...
38	4.1	1441.0	1300.0	False	False	False	1.75	0.0	...
50	3.8	30.0	800.0	False	False	False	0.00	0.0	...
65	2.7	49.0	800.0	False	False	False	0.00	0.0	...
75	3.6	98.0	300.0	True	False	False	0.00	0.0	...

	rating	rating_count	cost_for_two	online_order	table_reservation	delivery_only	time_00	time_01	time_02	time_03	...	Indonesian	Coffee	Middle Eastern	Sri Lankan	Singaporean	Raw Meats	Hot dogs	Assamese	Parsi	Burmese
27	0.048110	-0.373010	-0.142731	0.808688	-0.409714	-0.327782	1.810611	2.588419	-0.354653	-0.255785	...	-0.034627	-0.069379	-0.049	-0.034627	-0.034627	-0.034627	-0.034627	-0.060048	-0.049	-0.034627
38	0.720576	0.781396	1.980567	-1.235088	-0.409714	-0.327782	0.003920	-0.399163	-0.354653	-0.255785	...	-0.034627	-0.069379	-0.049	-0.034627	-0.034627	-0.034627	-0.034627	-0.060048	-0.049	-0.034627
50	0.048110	-0.372192	0.653506	-1.235088	-0.409714	-0.327782	-0.598310	-0.399163	-0.354653	-0.255785	...	-0.034627	-0.069379	-0.049	-0.034627	-0.034627	-0.034627	-0.034627	-0.060048	-0.049	-0.034627
65	-2.417597	-0.356659	0.653506	-1.235088	-0.409714	-0.327782	-0.598310	-0.399163	-0.354653	-0.255785	...	-0.034627	-0.069379	-0.049	-0.034627	-0.034627	-0.034627	-0.034627	-0.060048	-0.049	-0.034627
75	-0.400200	-0.316598	-0.673555	0.808688	-0.409714	-0.327782	-0.598310	-0.399163	-0.354653	-0.255785	...	-0.034627	-0.069379	-0.049	-0.034627	-0.034627	-0.034627	-0.034627	-0.060048	-0.049	-0.034627